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23
Categorising Social Tags to Improve Folksonomy-based Recommendations
- Journal of Web Semantics
, 2011
"... In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time ..."
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In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks. Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes. We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. The obtained concepts are then
Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations
"... {mns2, ha} @ ecs.soton.ac.uk Abstract. Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can ..."
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{mns2, ha} @ ecs.soton.ac.uk Abstract. Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals ’ tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites.
Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
"... The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid re ..."
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The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity. 1.
Semantic Contextualisation in a News Recommender System
"... The elements that can be considered under the notion of context in a recommender system are manifold: user tasks/goals, recently browsed/rated items, computing platforms and network conditions, social environment, physical environment and location, time, external events, etc. Complementarily to thes ..."
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The elements that can be considered under the notion of context in a recommender system are manifold: user tasks/goals, recently browsed/rated items, computing platforms and network conditions, social environment, physical environment and location, time, external events, etc. Complementarily to these elements, we propose a particular notion of context for semantic content retrieval: that of semantic runtime context, which we define as the background topics under which activities of a user occur within a given unit of time. A runtime context is represented in our approach as a set of weighted concepts from domain ontologies, obtained by collecting the concepts that have been involved in user’s actions (e.g., accessed items) during a session. Once the context is built, a contextual activation of user preferences is achieved by finding semantic paths linking preferences to context. In this paper, we present a user-centred study of our context-aware recommendation model using a news recommender system called News@hand. We analyse the strengths and weaknesses of our approach, and discuss the importance of contextualisation in a news recommendation scenario.
Generic Adaptation Framework: a Process-Oriented Perspective
"... Adaptive Hypermedia Systems (AHS) have long been mostly domain- or application-specific systems. Existing data structures and only briefly describe the adaptation process in a generic way. In this paper we consider the process aspects of AHS from the very first classical user modelling-adaptation lo ..."
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Adaptive Hypermedia Systems (AHS) have long been mostly domain- or application-specific systems. Existing data structures and only briefly describe the adaptation process in a generic way. In this paper we consider the process aspects of AHS from the very first classical user modelling-adaptation loop to a generic detailed flowchart of the adaptation in AHS. We introduce a Generic Adaptation Process (GAP) and by aligning it with a layered (data-oriented) AHS architecture we show that it can serve as the process part of a new reference model for AHS. 1
Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques
"... Abstract. Personalised recommender systems learn about a user’s needs, and identify and suggest information items (news articles, images, videos, etc.) that meet those needs. User needs can be explicitly or implicitly defined either in the form of user tastes, interests and goals, or by system param ..."
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Abstract. Personalised recommender systems learn about a user’s needs, and identify and suggest information items (news articles, images, videos, etc.) that meet those needs. User needs can be explicitly or implicitly defined either in the form of user tastes, interests and goals, or by system parameters and configurations. Most research efforts in the Recommender Systems field can be said to have been directed towards either defining and improving techniques that provide item recommendations from available preference data, or defining techniques for learning the latter. However, little research has focussed on learning which preferences are really relevant to provide accurate recommendations, and which ones imply anomalous behaviour of the recommendation mechanisms. We present a meta-evaluation methodology that applies Machine Learning techniques to analyse log information of a personalised news recommender system in order to discover (and rank) which user preferences and system settings are suitable for accurate recommendations. We also show how the proposed methodology can be used to ease the system evaluation itself.
A Linked Data Recommender System using a Neighborhood-based Graph Kernel
"... Abstract. The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively han-dle it. The boom ..."
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Abstract. The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively han-dle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed ker-nel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches. 1
An Enhanced Semantic Layer for Hybrid Recommender Systems: Application to News Recommendation
- Int. J. Semantic Web Inf. Syst
"... Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities or goals. Challenging issues in their research agenda inc ..."
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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities or goals. Challenging issues in their research agenda include the sparsity of user preference data, and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. We propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities, and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.
Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia
"... Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that compute ..."
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Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project. 1
Mobile Movie Recommendations with Linked Data
"... Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that compute ..."
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Cited by 3 (1 self)
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Abstract. The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages graph information within DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project.
